The present invention related generally to an adaptive receiver apparatus. More specifically, the invention relates to an adaptive receiver apparatus which adaptively equalizes reception signals following variations of channel characteristics in time sequence in a digital mobile communication system.
In a digital mobile communication system, since a radio wave is received through a plurality of paths, it is important to overcome the problem of irregular variations of the levels of reception signals, i.e. the so-called problem of multi-path fading during traveling. In particular, when the lag time difference between reflected waves reaching a receiver apparatus is large relative to a time interval of a transmission signal cannot be ignored. Accordingly, an adaptive equalization technology for equalizing wave form distortion following variations of channel characteristics is an important technology.
An adaptive equalizer initially derives a channel impulse response upon reception of known data sequence signals. Thereafter, the adaptive equalizer sequentially updates the channel impulse response employing an adaptive algorithm, such as the LMS (Least-Means-Squares) algorithm, RLS (Recursive Least-Squares) algorithm, and so forth, using decision signals as reference data sequence signals, upon reception of information data sequence signals for following sequential variations of the channel characteristics. The adaptive algorithm is discussed in C. F. N. Cowan and P. M. Grant, "Adaptive Filters", Prentice-Hall, Inc. England, 1985, for example. The disclosure in the above-identified publication is herein incorporated by reference for the sake of disclosure. However, in the adaptive equalization algorithm, it has been known to trade-off between a tracking speed (converging speed) and a tracking precision (residual error upon convergence). For example, in the LMS algorithm, when a correction coefficient, i.e. step size parameter defining a tracking property, is set at a relatively large value, high tracking speed can be achieved while tracking precision is lowered. On the other hand, when the step size parameter is set at a relatively small value, high tracking precision can be achieved by sacrificing the tracking speed. Similarly, in the case of the RLS algorithm, by setting a forgetting factor at a relatively small valve, high tracking speed can be achieved while the tracking precision is lowered. On the other hand, when the forgetting factor is increased to be close to 1 , the tracking precision can be increased while the tracking speed is sacrificed.
In mobile communication, a traveling speed of a mobile station is not constant and, rather, varies sequentially. It is also known to attain excellent equalization characteristics by providing higher priority for the tracking precision rather than the tracking speed at a low traveling speed where a time-variation is moderate, and provide higher priority for the tracking speed rather than the tracking precision at a high traveling speed where the time-variation is rapid. For example, relevant discussion has been given in E. Eleftheriou and D. D. Falconer, "Tracking Properties and Steady-State Performance of RLS Adaptive Filter Algorithms", IEEE Transaction on Acoustics, Speech and Signal Processing, vol. ASSP-34, No. 5, pp 1097.about.1110, October, 1986. Accordingly, there exist no optimal values for the coefficient defining the tracking property at any condition, to make it difficult to preliminarily set this coefficient. In other words, even when the coefficient is set at one factor, it is not possible to attain the optimal equalization effect.